DSSD : Deconvolutional Single Shot Detector

Introduction

Deconvolutional SSD brings additional context into state-of-the-art general object detection by adding extra deconvolution structures. The DSSD achieves much better accuracy on small objects compared to SSD.

The code is based on SSD. For more details, please refer to our arXiv paper.

Installation

1. Download the code from github. We call this directory as $CAFFE_ROOT later.

   git clone https://github.com/chengyangfu/caffe.git
   cd $CAFFE_ROOT
   git checkout dssd

2. Build the code. Please follow Caffe instruction to install all necessary packages and build it.

   # Modify Makefile.config according to your Caffe installation.
   cp Makefile.config.example Makefile.config
   make -j8
   # Make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
   make py
   make test -j8
   # (Optional)
   make runtest -j

Preparation

1. Please Follow the Orginal SSD to do all the preparation works. You should have lmdb fils for VOC2007. Check the following two links exist or not.

   ls $CAFFE_ROOT/examples
   # $CAFFE_ROOT/examples/VOC0712/VOC0712_trainval_lmdb
   # $CAFFE_ROOT/examples/VOC0712/VOC0712_test_lmdb

2. Download the Resnet-101 models from the Deep-Residual-Network.

   # creat the directory for ResNet-101
   cd $CAFFE_ROOT/models
   mkdir ResNet-101
   # Rename the Resnet-101 models and put in the ResNet-101 direcotry
   ls $CAFFE_ROOT/models/ResNet-101
   # $CAFFE_ROOT/models/ResNet-101/ResNet-101-model.caffemodel
   # $CAFFE_ROOT/models/ResNet-101/ResNet-101-deploy.prototxt

Train/Eval

1. Train and Eval the SSD model

   # Train the SSD-ResNet-101 321x321
   python examples/ssd/ssd_pascal_resnet_321.py
   # GPU setting may need be change according to the numbers of gpu 
   # models are generated in:
   # $CAFFE_ROOT/models/ResNet-101/VOC0712/SSD_VOC07_321x321
   # Evaluate the model
   cd $CAFFE_ROOT
   ./build/tools/caffe train --solver="./models/ResNet-101/VOC0712/SSD_VOC07_321x321/test_solver.prototxt"  \
   --weights="./models/ResNet-101/VOC0712/SSD_VOC07_321x321/ResNet-101_VOC0712_SSD_VOC07_321x321_iter_80000.caffemodel" \
   --gpu=0
   # batch size in the test.prototxt may need be changed.
   # If the batch size is changed, remeber to change the test_iter in test_solver.prototxt at same time. 
   # It should reach 77.5* mAP at 80k iterations.

2. Train and Evaluate the DSSD model. In this script, Resnet-101 and SSD related layers are frozen and only the DSSD related layers are trained.

   # Use the SSD-ResNet-101 321x321 as the pretrained model
   python examples/ssd/ssd_pascal_resnet_deconv_321.py
   # Evaluate the model
   cd $CAFFE_ROOT
   ./build/tools/caffe train --solver="./models/ResNet-101/VOC0712/DSSD_VOC07_321x321/test_solver.prototxt"  \
   --weights="./models/ResNet-101/VOC0712/DSSD_VOC07_321x321/ResNet-101_VOC0712_DSSD_VOC07_321x321_iter_30000.caffemodel" \
   --gpu=0
   # It should reach 78.6* mAP at 30k iterations.

3. Train and Evalthe DSSD model. In this script, we try to fine-tune the entire network. In order to sucessfully finetune the network, we need to freeze all the batch norm related layers in Caffe.

   # Use the DSSD-ResNet-101 321x321 as the pretrained model
   python examples/ssd/ssd_pascal_resnet_deconv_ft_321.py
   # Evaluate the model
   cd $CAFFE_ROOT
   ./build/tools/caffe train --solver="./models/ResNet-101/VOC0712/DSSD_VOC07_FT_321x321/test_solver.prototxt"  \
   --weights="./models/ResNet-101/VOC0712/DSSD_VOC07_FT_321x321/ResNet-101_VOC0712_DSSD_VOC07_FT_321x321_iter_40000.caffemodel" \
   --gpu=0
   # Finetuning the entire network only works for the model with 513x513 inputs not 321x321.